Analysis of ‘International Agricultural Exports’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9cb78ed2-0b3c-41f2-87e5-0d07eb2355e3 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Increase the 4-year moving average of total international agricultural exports from $1.67 billion in 2012 to $1.76 billion by 2018.

--- Original source retains full ownership of the source dataset ---

Factori's AI & ML training data is thoroughly tested and reviewed to ensure that what you receive on your end is of the best quality.

Integrate the comprehensive AI & ML training data provided by Grepsr and develop a superior AI & ML model.

Whether you're training algorithms for natural language processing, sentiment analysis, or any other AI application, we can deliver comprehensive datasets tailored to fuel your machine learning initiatives.

Enhanced Data Quality: We have rigorous data validation processes and also conduct quality assurance checks to guarantee the integrity and reliability of the training data for you to develop the AI & ML models.

Gain a competitive edge, drive innovation, and unlock new opportunities by leveraging the power of tailored Artificial Intelligence and Machine Learning training data with Factori.

We offer web activity data of users that are browsing popular websites around the world. This data can be used to analyze web behavior across the web and build highly accurate audience segments based on web activity for targeting ads based on interest categories and search/browsing intent.

Web Data Reach: Our reach data represents the total number of data counts available within various categories and comprises attributes such as Country, Anonymous ID, IP addresses, Search Query, and so on.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Data Attributes: Anonymous_id IDType Timestamp Estid Ip userAgent browserFamily deviceType Os Url_metadata_canonical_url Url_metadata_raw_query_params refDomain mappedEvent Channel searchQuery Ttd_id Adnxs_id Keywords Categories Entities Concepts

Analysis of ‘Transportation And Distribution Exports’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/683640f1-84a3-402c-b6a7-bd033656025a on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Increase Transportation and Distribution exports from $16.799 billion in 2013 to $18.908 billion by 2017.

--- Original source retains full ownership of the source dataset ---

Our POI Data connects people's movements to over 200M+ physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

Reach: Our POI/Place/OOH level insights are calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Cases: Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Geo-Targeted Advertising: Utilizing location-based insights, businesses can deliver highly personalized advertisements to consumers based on their proximity to relevant POIs.

Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience.

Site Selection: By assessing the proximity to relevant POIs such as competitors, customer demographics, and economic indicators, organizations can make informed decisions about opening new locations.

OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively.

Data Attributes Included: poi_id name category_id is_claimed photo_url brand name brand_id places_topics people_also_search local_business_links naics_code naics_code_description sis_code sic_code_description shape_polygon building_id geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8 building_type building_name shape_type reviews count contact_info local_business_links work_hours popular_time total_photos status attributes price_level rating domain url phone additional_categories longitude latitude country_code zip state city full_address description

This table contains 345 series, with data for years 1968 - 1992 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Trading area (2 items: Union of Soviet Socialist Republics (U.S.S.R.); Puerto Rico) Summary export groups (SEG) and other aggregations (185 items: Total exports; Total re-exports; Total domestic exports; Total live animals; ...).

Analysis of ‘VCA18 - Food and Drink Exports for January & February’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/f25a755a-a2ea-4c1b-a1f7-5e6e9555ec08 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Food and Drink Exports for January & February

--- Original source retains full ownership of the source dataset ---

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This table contains 4777 series, with data for years 1963 - 2004 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Destination (25 items: All countries; European Union 1995; Other Organization for Economic Co-operation and Development countries (excluding the European Union 1995, United States of America and Japan); Other countries (excluding the European Union 1995, other Organization for Economic Co-operation and Development countries, United States of America and Japan); ...); Summary export groups (SEG) and other aggregations (192 items: Total exports; Total re-exports; Total domestic exports;Total live animals; ...).

Success.ai’s Import Export Data for Import, Export & Trade Professionals in Asia delivers a comprehensive dataset tailored for businesses aiming to connect with key players in Asia’s dynamic trade industry. Covering professionals involved in import/export operations, international logistics, and supply chain management, this dataset provides verified contact details, firmographic insights, and actionable professional data.

With access to over 700 million verified global profiles and 70 million business datasets, Success.ai ensures your outreach, market research, and trade strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the complexities of global trade in Asia.

Why Choose Success.ai’s Import Export Data?

1. Verified Contact Data for Effective Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of trade professionals, logistics experts, and supply chain managers.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication efficiency.

2. Comprehensive Coverage of Asian Trade Markets

   • Includes profiles of professionals from key Asian markets such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional trade trends, import/export regulations, and supply chain dynamics.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, trade activities, and market expansions.
   • Stay aligned with evolving market conditions and emerging trade opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with import/export professionals, logistics managers, and trade consultants across Asia.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Contact Details: Gain verified work emails, phone numbers, and business locations for precise targeting.
• Industry Trends: Understand key import/export opportunities, supply chain challenges, and market dynamics in Asia.

Key Features of the Dataset:

1. Professional Profiles in Import/Export and Logistics

   • Identify and engage with trade professionals managing cross-border operations, customs compliance, and supply chain efficiency.
   • Target individuals responsible for vendor selection, international partnerships, and trade negotiations.

2. Firmographic and Geographic Insights

   • Access data on company structures, trade volumes, and operational hubs in key Asian markets.
   • Pinpoint high-value prospects in established and emerging trade routes for strategic engagement.

3. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (manufacturing, wholesale, retail), geographic location, or revenue size.
   • Tailor campaigns to address specific trade needs such as market entry, cost optimization, or regulatory compliance.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with trade professionals.

Strategic Use Cases:

1. Sales and Business Development

   • Present trade services, logistics solutions, or supply chain optimization tools to import/export managers and trade consultants.
   • Build relationships with procurement teams and logistics managers seeking reliable partners and innovative solutions.

2. Market Research and Competitive Analysis

   • Analyze trends in Asia’s import/export landscape, including key trade routes, regulatory changes, and logistics challenges.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and emerging needs.

3. Partnership Development and Trade Collaboration

   • Engage with businesses seeking partnerships for supply chain management, customs compliance, or international expansion.
   • Foster alliances that enhance efficiency, reduce costs, and drive growth in the import/export sector.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in international trade, logistics, or operations.
   • Provide workforce optimization platforms or training solutions tailored to the trade and logistics industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality import/export data at competitive prices, ensuring maximum ROI for your marketing, sales, and trade initiatives.

2. Seamless Integration

   • Integrate verified trade data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and ...

Techsalerator’s Import/Export Trade Data for Asia

Techsalerator’s Import/Export Trade Data for Asia offers a comprehensive and detailed examination of trade activities across the Asian continent. This extensive dataset provides deep insights into import and export transactions involving companies across various sectors throughout Asia.

Coverage Across All Asian Countries

The dataset encompasses a broad range of countries within Asia, including:

Central Asia:

Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan East Asia:

China Hong Kong Japan Mongolia North Korea South Korea Taiwan Southeast Asia:

Brunei Cambodia East Timor (Timor-Leste) Indonesia Laos Malaysia Myanmar (Burma) Philippines Singapore Thailand Vietnam South Asia:

Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka West Asia (Middle East):

Armenia Azerbaijan Bahrain Cyprus Georgia Iran Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey United Arab Emirates Yemen Comprehensive Data Features

Transaction Details: The dataset includes detailed information on individual trade transactions, such as product descriptions, quantities, values, and dates. This level of detail allows for accurate tracking and analysis of trade patterns across Asia.

Company Information: It provides insights into the companies involved in trade, including their names, locations, and industry sectors. This information supports targeted market analysis and competitive intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, helping users understand market dynamics and sector-specific trends across diverse Asian economies.

Trade Trends: Historical data is available to analyze trade trends, identify emerging markets, and assess the impact of economic or geopolitical events on trade flows within the region.

Geographical Insights: Users can explore regional trade flows and cross-border dynamics between Asian countries and their global trade partners, including major trading nations outside the continent.

Regulatory and Compliance Data: Information on trade regulations, tariffs, and compliance requirements is included, assisting businesses in navigating the complex regulatory environments across different Asian countries.

Applications and Benefits

Market Research: Businesses can use the data to identify new market opportunities, assess competitive landscapes, and understand consumer demand across various Asian countries.

Strategic Planning: Companies can leverage insights from the data to refine trade strategies, optimize supply chains, and manage risks associated with international trade in Asia.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development initiatives.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in Asia’s diverse and rapidly evolving markets.

Techsalerator’s Import/Export Trade Data for Asia provides a vital resource for organizations involved in international trade, offering a detailed, reliable, and expansive view of trade activities across the Asian continent.

Analysis of ‘STA06 – Services Imports and exports ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/a0e59b17-e616-4a9f-b338-4716de83691f on 17 January 2022.

--- Dataset description provided by original source is as follows ---

Services Imports and exports

--- Original source retains full ownership of the source dataset ---

The Export Raw Scopus Data on The Integration of Artificial Intelligence in Learning for Early Childhood Educationcontains a collection of bibliometric data extracted from the Scopus database. This dataset includes information on research publications, citation counts, author contributions, institutional affiliations, and keyword trends related to the application of artificial intelligence (AI) in early childhood education (ECE).The dataset covers studies from 2003 to 2025, providing insights into how AI has been integrated into early childhood learning environments over time. Key components of this data include:Publication Details: Title, authors, publication year, source journals, and digital object identifiers (DOI).Citation Metrics: Number of citations per article, h-index, and impact factor of sources.Author and Institutional Contributions: Leading researchers, universities, and research centers involved in AI and ECE studies.Keyword Analysis: Frequently used terms such as Artificial Intelligence, Early Childhood Education, Learning Systems, and Machine Learning, highlighting emerging research trends.Collaboration Networks: Country-wise and institutional co-authorship patterns, indicating global research collaborations.This raw Scopus export is essential for conducting bibliometric analysis, identifying research gaps, and exploring future directions in AI-driven early childhood education. It serves as a valuable resource for researchers, policymakers, and educators interested in understanding the evolution and impact of AI in early childhood learning.

This dataset originates from a series of experimental studies titled “Tough on People, Tolerant to AI? Differential Effects of Human vs. AI Unfairness on Trust” The project investigates how individuals respond to unfair behavior (distributive, procedural, and interactional unfairness) enacted by artificial intelligence versus human agents, and how such behavior affects cognitive and affective trust.1 Experiment 1a: The Impact of AI vs. Human Distributive Unfairness on TrustOverview: This dataset comes from an experimental study aimed at examining how individuals respond in terms of cognitive and affective trust when distributive unfairness is enacted by either an artificial intelligence (AI) agent or a human decision-maker. Experiment 1a specifically focuses on the main effect of the “type of decision-maker” on trust.Data Generation and Processing: The data were collected through Credamo, an online survey platform. Initially, 98 responses were gathered from students at a university in China. Additional student participants were recruited via Credamo to supplement the sample. Attention check items were embedded in the questionnaire, and participants who failed were automatically excluded in real-time. Data collection continued until 202 valid responses were obtained. SPSS software was used for data cleaning and analysis.Data Structure and Format: The data file is named “Experiment1a.sav” and is in SPSS format. It contains 28 columns and 202 rows, where each row corresponds to one participant. Columns represent measured variables, including: grouping and randomization variables, one manipulation check item, four items measuring distributive fairness perception, six items on cognitive trust, five items on affective trust, three items for honesty checks, and four demographic variables (gender, age, education, and grade level). The final three columns contain computed means for distributive fairness, cognitive trust, and affective trust.Additional Information: No missing data are present. All variable names are labeled in English abbreviations to facilitate further analysis. The dataset can be directly opened in SPSS or exported to other formats.2 Experiment 1b: The Mediating Role of Perceived Ability and Benevolence (Distributive Unfairness)Overview: This dataset originates from an experimental study designed to replicate the findings of Experiment 1a and further examine the potential mediating role of perceived ability and perceived benevolence.Data Generation and Processing: Participants were recruited via the Credamo online platform. Attention check items were embedded in the survey to ensure data quality. Data were collected using a rolling recruitment method, with invalid responses removed in real time. A total of 228 valid responses were obtained.Data Structure and Format: The dataset is stored in a file named Experiment1b.sav in SPSS format and can be directly opened in SPSS software. It consists of 228 rows and 40 columns. Each row represents one participant’s data record, and each column corresponds to a different measured variable. Specifically, the dataset includes: random assignment and grouping variables; one manipulation check item; four items measuring perceived distributive fairness; six items on perceived ability; five items on perceived benevolence; six items on cognitive trust; five items on affective trust; three items for attention check; and three demographic variables (gender, age, and education). The last five columns contain the computed mean scores for perceived distributive fairness, ability, benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be analyzed directly in SPSS or exported to other formats as needed.3 Experiment 2a: Differential Effects of AI vs. Human Procedural Unfairness on TrustOverview: This dataset originates from an experimental study aimed at examining whether individuals respond differently in terms of cognitive and affective trust when procedural unfairness is enacted by artificial intelligence versus human decision-makers. Experiment 2a focuses on the main effect of the decision agent on trust outcomes.Data Generation and Processing: Participants were recruited via the Credamo online survey platform from two universities located in different regions of China. A total of 227 responses were collected. After excluding those who failed the attention check items, 204 valid responses were retained for analysis. Data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2a.sav in SPSS format and can be directly opened in SPSS software. It contains 204 rows and 30 columns. Each row represents one participant’s response record, while each column corresponds to a specific variable. Variables include: random assignment and grouping; one manipulation check item; seven items measuring perceived procedural fairness; six items on cognitive trust; five items on affective trust; three attention check items; and three demographic variables (gender, age, and education). The final three columns contain computed average scores for procedural fairness, cognitive trust, and affective trust.Additional Notes: The dataset contains no missing values. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be directly analyzed in SPSS or exported to other formats as needed.4 Experiment 2b: Mediating Role of Perceived Ability and Benevolence (Procedural Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 2a and to further examine the potential mediating roles of perceived ability and perceived benevolence in shaping trust responses under procedural unfairness.Data Generation and Processing: Participants were working adults recruited through the Credamo online platform. A rolling data collection strategy was used, where responses failing attention checks were excluded in real time. The final dataset includes 235 valid responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2b.sav, which is in SPSS format and can be directly opened using SPSS software. It contains 235 rows and 43 columns. Each row corresponds to a single participant, and each column represents a specific measured variable. These include: random assignment and group labels; one manipulation check item; seven items measuring procedural fairness; six items for perceived ability; five items for perceived benevolence; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final five columns contain the computed average scores for procedural fairness, perceived ability, perceived benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to support future reuse and secondary analysis. The dataset can be directly analyzed in SPSS and easily converted into other formats if needed.5 Experiment 3a: Effects of AI vs. Human Interactional Unfairness on TrustOverview: This dataset comes from an experimental study that investigates how interactional unfairness, when enacted by either artificial intelligence or human decision-makers, influences individuals’ cognitive and affective trust. Experiment 3a focuses on the main effect of the “decision-maker type” under interactional unfairness conditions.Data Generation and Processing: Participants were college students recruited from two universities in different regions of China through the Credamo survey platform. After excluding responses that failed attention checks, a total of 203 valid cases were retained from an initial pool of 223 responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3a.sav, in SPSS format and compatible with SPSS software. It contains 203 rows and 27 columns. Each row represents a single participant, while each column corresponds to a specific measured variable. These include: random assignment and condition labels; one manipulation check item; four items measuring interactional fairness perception; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final three columns contain computed average scores for interactional fairness, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variable names are provided using standardized English abbreviations to facilitate secondary analysis. The data can be directly analyzed using SPSS and exported to other formats as needed.6 Experiment 3b: The Mediating Role of Perceived Ability and Benevolence (Interactional Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 3a and further examine the potential mediating roles of perceived ability and perceived benevolence under conditions of interactional unfairness.Data Generation and Processing: Participants were working adults recruited via the Credamo platform. Attention check questions were embedded in the survey, and responses that failed these checks were excluded in real time. Data collection proceeded in a rolling manner until a total of 227 valid responses were obtained. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3b.sav, in SPSS format and compatible with SPSS software. It includes 227 rows and

Mobility data is collected through location-aware mobile apps using an SDK-based implementation. Users explicitly consent to share their location data via a clear opt-in process and are provided with clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure the highest quality data is available for analysis.

• Record Count: 90 Billion
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Mobility Data Reach

Our data reach encompasses the total counts available across various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology

We collect data dynamically, offering the most updated data and insights at the best-suited intervals (daily, weekly, monthly, or quarterly).

Business Needs

Our data supports various business needs, including consumer insight, market intelligence, advertising, and retail analytics.

Techsalerator’s Import/Export Trade Data for Europe

Techsalerator’s Import/Export Trade Data for Europe offers a meticulously detailed and expansive analysis of trade activities across the European continent. This robust data resource provides an in-depth examination of import and export transactions involving companies in a wide range of industries within the European Union (EU) and the broader European region.

Coverage Across All EU Countries

The dataset encompasses all 27 EU member countries, ensuring a comprehensive overview of trade dynamics across the region. This includes:

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden Comprehensive Data Features

Transaction Details: Each record in the dataset provides granular details on individual trade transactions, including the nature of goods or services exchanged, quantities, values, and transaction dates.

Company Information: Data includes specific information about the trading companies involved, such as company names, addresses, and sectors, allowing for targeted analysis and business insights.

Categorization: Transactions are categorized by industry sectors, product categories, and trade partners, providing clarity on market trends and sector-specific performance.

Trade Trends: The dataset includes historical trade trends and patterns, helping users analyze shifts in trade volumes, emerging markets, and economic impacts over time.

Geographical Insights: Users can explore regional trade flows and cross-border trade dynamics within the EU and with non-EU European countries.

Regulatory and Compliance Data: Information on relevant trade regulations, tariffs, and compliance requirements is included, assisting businesses in navigating the complex regulatory landscape of international trade.

Applications and Benefits

Market Research: Businesses can leverage the data to identify new market opportunities, track competitor activities, and assess the demand for specific products across different European regions.

Strategic Planning: Companies can use the insights to develop more effective trade strategies, optimize supply chains, and manage risks associated with international trade.

Economic Analysis: Analysts and policymakers can use the data to monitor economic performance, understand trade imbalances, and make informed decisions on trade policies and economic development initiatives.

Techsalerator’s Import/Export Trade Data for Europe is a vital tool for any organization involved in international trade, providing a detailed, reliable, and comprehensive view of the trade landscape across Europe.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Techsalerator’s Import/Export Trade Data for Latin America

Techsalerator’s Import/Export Trade Data for Latin America delivers an extensive and detailed analysis of trade activities throughout the Latin American region. This comprehensive dataset provides valuable insights into import and export transactions involving companies across various sectors within Latin America.

Coverage Across All Latin American Countries

The dataset encompasses all countries in Latin America, including:

Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Additionally, it includes countries in Central America and the Caribbean:

Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Cuba Dominican Republic Haiti Jamaica Trinidad and Tobago Comprehensive Data Features

Transaction Details: The dataset provides detailed information on individual trade transactions, including product descriptions, quantities, values, and dates. This allows for precise tracking of trade flows and patterns.

Company Information: It includes specific details about the companies involved in trade, such as company names, locations, and industry sectors, facilitating targeted market research and business analysis.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners. This helps in understanding market dynamics and sector-specific trends within the region.

Trade Trends: Users can analyze historical data to observe trends and shifts in trade volumes, identify emerging markets, and assess the impact of economic or political events on trade patterns.

Geographical Insights: The data offers insights into regional trade flows and the relationships between Latin American countries and their global trade partners, including major trading nations outside the region.

Regulatory and Compliance Data: The dataset includes information on trade regulations, tariffs, and compliance requirements, aiding businesses in navigating the regulatory landscape of international trade within Latin America.

Applications and Benefits

Market Research: Businesses can utilize the data to uncover new market opportunities, analyze competitive landscapes, and understand consumer demand across various Latin American countries.

Strategic Planning: Companies can leverage insights from the data to refine trade strategies, optimize supply chains, and mitigate risks associated with international trade in the region.

Economic Analysis: Analysts and policymakers can use the data to monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development initiatives.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in Latin America’s diverse economies.

Techsalerator’s Import/Export Trade Data for Latin America provides a crucial resource for organizations involved in international trade, offering a detailed, reliable, and expansive view of trade activities across the Latin American continent.

Analysis of ‘TSM04 - Exports of Cattle and Beef’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ad361977-bc55-4427-a39f-50785fe860db on 10 January 2022.

--- Dataset description provided by original source is as follows ---

Exports of Cattle and Beef

--- Original source retains full ownership of the source dataset ---

Introduction/objective: Digitalization in logistics transcended in the search for continuous improvement of good process optimization. This study aims to know the effectiveness of digitization implemented by companies to improve the automated logistics of cross-border trade in the agricultural sector.

Methodology: A bibliometric analysis was generated, exploring the evolution of the state of the art through Scopus, WOS and Dimensions databases, in order to select relevant empirical studies on digitization and automated logistics, using quality criteria and the application of the Prisma 2020 flowchart.

Results: Since 2017, there were signs of increased interest from researchers, highlighting authors such as Zoubek, Kumar and Ghobakhloo. This review provided insight into how digitization contributes to cost and time optimization in the logistics chain. Designing public policies allows a better integration of technology, such as IoT and AI. It identified 3 important blocks that have contributed to the effectiveness of digitization in automated logistics, they refer to “Impact of digitization on logistics efficiency and supply chain”, “Technology integration and automation in cross-border logistics” and “Governance, policy and social considerations in logistics digitization”.

Conclusions: Digitalization has been a fundamental element to improve logistics and make it autonomous within cross-border trade, allowing technology to get involved, integrating digital technologies such as artificial intelligence (AI), which reduced obstacles affecting the supply chain.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.